Lecture
This lecture covers the basics of Simultaneous Localization and Mapping (SLAM), focusing on the process of incrementally building a map of an unknown environment while determining the robot's location. Topics include different map types such as real maps, line-based maps, occupancy grid maps, and topological maps. The lecture also discusses map creation techniques like the Split & Merge technique, map refinement, feature addition, and removal. It explores the prediction of measurements and positions using odometry estimation and sensor fusion. Additionally, it delves into the concept of features matching and various approaches to SLAM, such as volumetric vs. feature-based, topological vs. metric, and active vs. passive map building.